Venous thrombosis in children
Bibliographic record
Abstract
Venous thromboembolic (VTE) events are being increasingly diagnosed in systemic and cerebral vessels in children. Systemic VTE are increasing in children as a result of therapeutic advances and improved clinical acumen in primary illnesses that previously caused mortality. The epidemiology of systemic VTE has been studied in international registries. In children older than 3 months, teenagers are the largest group developing VTE. The most common etiologic factor is the presence of central venous lines. Clinical studies have determined the most sensitive diagnostic method for diagnosing upper system VTE are ultrasound for jugular venous thrombosis and venography for intrathoracic vessels. However, the most sensitive diagnostic methods for lower system VTE and pulmonary embolism (PE) have not been established. Treatment studies for VTE consist of inadequately powered randomized controlled trials or prospective cohort studies. The long term outcome of systemic VTE, post-thrombotic syndrome, has been reported in children. Cerebral sinovenous thrombosis (CSVT) is becoming increasingly diagnosed in children due to the recognition of the associated subtle clinical symptoms and improved cerebrovascular imaging. The etiology of CSVT includes thrombophilia, head and neck infections, and systemic illness. Estimates of the incidence and outcome of childhood CSVT have recently become available through the Canadian Pediatric Ischaemic Stroke Registry. Clinical studies have not yet been carried out in children to determine the best method of diagnosis or treatment. There have only been caseseries studies carried out in the treatment of CSVT. Properly designed clinical trials are urgently required in children with systemic VTE/PE and CSVT to define the best methods of diagnosis, treatment and long-term management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".